Top-p Sampling
Top-p sampling (nucleus sampling) selects from the smallest set of tokens whose cumulative probability exceeds a threshold p. It dynamically adjusts the candidate pool size based on the model's confidence.
Understanding Top-p Sampling
Top-p sampling, also known as nucleus sampling, dynamically selects the smallest set of tokens whose cumulative probability exceeds a threshold p, then samples from this set. Unlike top-k sampling, which always considers a fixed number of candidates, top-p adapts the candidate pool size based on the model's confidence at each step. When the model is certain, fewer tokens are considered; when uncertain, more options remain available. A typical p value of 0.9 means the model samples from tokens comprising 90% of the probability mass, effectively filtering out the long tail of unlikely tokens. This adaptive behavior produces more natural and coherent text generation compared to fixed-k approaches. Top-p sampling has become the default decoding strategy in many large language model APIs alongside temperature control.
Category
Generative AI
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